Mobile electronic devices such as handheld devices, media players, cellular phones, smartphones, and other tablet-based devices are rapidly becoming ubiquitous throughout the world. More and more sensors are being embedded into mobile electronic devices to enable a new generation of personal and environmental context aware applications. Typical phone placement contexts include “in pocket” (inPocket), “in bag” (inBag), “out of pocket or bag”, “in hand” (inHand), or “on table” (onTable). Efficient recognition of these low-level contexts on the device is a fundamental building block for other new emerging sensing applications.
Recognizing the placement contexts can improve the accuracy of recognizing other contexts. For example, if a phone is detected out of pocket or bag, the onTable context can be better detected by further estimating the gravity vector on a surface and signal magnitude variance from accelerometer sensor, and the inHand context can be better detected by further inspecting signal vibration signatures from accelerometer and gyroscope sensors. In accelerometer-based physical activity recognition, the accelerometer sensor generates different signals when the phone is in a pocket, in a bag, or out of them. If the placement context of the phone is known, a placement-dependent algorithm can be created to improve the recognition accuracy. Moreover, the inPocket context recognition can enable a new mode called “pocket mode” in which the phone screen can be automatically locked, the volume of an incoming call automatically increased and vibration turned on. Similarly, if the phone is detected in a bag (“bag mode”), the ringtone time for an incoming all can be automatically increased to give users enough time to take the phone out.
Because most continuous context aware applications running on mobile phones are resource intensive and power consuming, there is a need to develop efficient recognition algorithms. Currently, proximity sensor, ultra-sonic sensor, light sensor, accelerometer sensor, and other sensing devices have all been applied to detect phone placement contexts. While there has been significant research efforts in the area of context awareness, it is desirable for an accurate, robust, and energy-efficient recognition algorithm that can automatically detect low-level phone placement contexts.